
'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Optional[int]				       =							"huggingface/label-files"
  UpperCAmelCase__      :					List[str]				       =							"imagenet-1k-id2label.json"
  UpperCAmelCase__      :					Union[str, Any]				       =							json.load(open(hf_hub_download(lowercase__   ,       lowercase__   ,       repo_type="dataset"		)   ,       "r"		)		)
  UpperCAmelCase__      :					List[str]				       =							{int(lowercase__		): v for k, v in idalabel.items()}
  UpperCAmelCase__      :					str				       =							{v: k for k, v in idalabel.items()}

  UpperCAmelCase__      :					Tuple				       =							"std_conv" if "bit" in model_name else False

  # note that when using BiT as backbone for ViT-hybrid checkpoints,
  # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
  # config.conv_layer = "std_conv_same"
  UpperCAmelCase__      :					Optional[int]				       =							BitConfig(
      conv_layer=lowercase__   ,       num_labels=1_0_0_0   ,       idalabel=lowercase__   ,       labelaid=lowercase__   ,       )

  return config







def snake_case_     (					lowercase__		):
  if "stem.conv" in name:
    UpperCAmelCase__      :					Optional[Any]				       =							name.replace("stem.conv"   ,       "bit.embedder.convolution"		)
  if "blocks" in name:
    UpperCAmelCase__      :					str				       =							name.replace("blocks"   ,       "layers"		)
  if "head.fc" in name:
    UpperCAmelCase__      :					int				       =							name.replace("head.fc"   ,       "classifier.1"		)
  if name.startswith("norm"		):
    UpperCAmelCase__      :					str				       =							"bit." + name
  if "bit" not in name and "classifier" not in name:
    UpperCAmelCase__      :					int				       =							"bit.encoder." + name

  return name







def snake_case_     (					):
  UpperCAmelCase__      :					List[Any]				       =							"http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCAmelCase__      :					str				       =							Image.open(requests.get(lowercase__   ,       stream=lowercase__		).raw		)
  return im







@torch.no_grad()
def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__=False		):
  UpperCAmelCase__      :					Tuple				       =							get_config(lowercase__		)

  # load original model from timm
  UpperCAmelCase__      :					Optional[Any]				       =							create_model(lowercase__   ,       pretrained=lowercase__		)
  timm_model.eval()

  # load state_dict of original model
  UpperCAmelCase__      :					int				       =							timm_model.state_dict()
  for key in state_dict.copy().keys():
    UpperCAmelCase__      :					str				       =							state_dict.pop(lowercase__		)
    UpperCAmelCase__      :					Union[str, Any]				       =							val.squeeze() if "head" in key else val

  # load HuggingFace model
  UpperCAmelCase__      :					Dict				       =							BitForImageClassification(lowercase__		)
  model.eval()
  model.load_state_dict(lowercase__		)

  # create image processor
  UpperCAmelCase__      :					str				       =							create_transform(**resolve_data_config({}   ,       model=lowercase__		)		)
  UpperCAmelCase__      :					Union[str, Any]				       =							transform.transforms

  UpperCAmelCase__      :					Optional[Any]				       =							{
      "bilinear": PILImageResampling.BILINEAR,
      "bicubic": PILImageResampling.BICUBIC,
      "nearest": PILImageResampling.NEAREST,
  }

  UpperCAmelCase__      :					List[Any]				       =							BitImageProcessor(
      do_resize=lowercase__   ,       size={"shortest_edge": timm_transforms[0].size}   ,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]   ,       do_center_crop=lowercase__   ,       crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}   ,       do_normalize=lowercase__   ,       image_mean=timm_transforms[-1].mean.tolist()   ,       image_std=timm_transforms[-1].std.tolist()   ,       )

  UpperCAmelCase__      :					int				       =							prepare_img()
  UpperCAmelCase__      :					Dict				       =							transform(lowercase__		).unsqueeze(0		)
  UpperCAmelCase__      :					Optional[Any]				       =							processor(lowercase__   ,       return_tensors="pt"		).pixel_values

  # verify pixel values
  assert torch.allclose(lowercase__   ,       lowercase__		)

  # verify logits
  with torch.no_grad():
    UpperCAmelCase__      :					Dict				       =							model(lowercase__		)
    UpperCAmelCase__      :					List[str]				       =							outputs.logits

  print("Logits:"   ,       logits[0, :3]		)
  print("Predicted class:"   ,       model.config.idalabel[logits.argmax(-1		).item()]		)
  UpperCAmelCase__      :					Dict				       =							timm_model(lowercase__		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(lowercase__   ,       outputs.logits   ,       atol=1E-3		)
  print("Looks ok!"		)

  if pytorch_dump_folder_path is not None:
    Path(lowercase__		).mkdir(exist_ok=lowercase__		)
    print(F"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""		)
    model.save_pretrained(lowercase__		)
    processor.save_pretrained(lowercase__		)

  if push_to_hub:
    print(F"""Pushing model {model_name} and processor to the hub"""		)
    model.push_to_hub(F"""ybelkada/{model_name}"""		)
    processor.push_to_hub(F"""ybelkada/{model_name}"""		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""resnetv2_50x1_bitm""",
          type=str,
          help="""Name of the BiT timm model you\'d like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""",
          action="""store_true""",
          help="""Whether to push the model to the hub.""",
      )

      SCREAMING_SNAKE_CASE     =       parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()